R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

R-project

https://cran.r-project.org/

RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

RStudio eklentileri

Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

# devtools::install_github("rstudio/addinexamples", type = "source")

X11

https://www.xquartz.org/

Java OS

https://support.apple.com/kb/dl1572

R zor şeyler için kolay, kolay şeyler için zor

R makes easy things hard, and hard things easy
Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf

R paketleri

Neden paketler var

I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

Paketleri nereden bulabiliriz

Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.html
Bioconductor
https://www.bioconductor.org
RecommendR
http://recommendr.info/
pkgsearch
CRAN package search
https://github.com/metacran/pkgsearch
Awesome R
https://awesome-r.com/

Kendi paket evrenini oluştur

pkgverse: Build a Meta-Package Universe
https://cran.r-project.org/web/packages/pkgverse/index.html

R için yardım bulma

# ?mean
# ??efetch
# help(merge)
# example(merge)

Vignette

RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow

https://stackoverflow.com/

Google uygun anahtar kelime

How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
— Emily Bovee (@ebovee09) August 10, 2018

Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/

Twitter

https://twitter.com/hashtag/rstats?src=hash

Reproducible Examples

Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX
— ZhiYang (@zhiiiyang) October 18, 2018

R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")

require(tidyverse)
require(jmv)
require(questionr)
library(summarytools)
library(gganimate)

R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

Excel

SPSS

csv

Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018

# library(nycflights13)
# summary(flights)

View(data)

data

head

tail

glimpse

str

skimr::skim()

Veriyi değiştirme

Veriyi kod ile değiştirelim

Veriyi eklentilerle değiştirme

RStudio aracılığıyla recode

questionr paketi kullanılacak

https://juba.github.io/questionr/articles/recoding_addins.html

Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

## Parsed with column specification:
## cols(
##   Sepal.Length = col_double(),
##   Sepal.Width = col_double(),
##   Petal.Length = col_double(),
##   Petal.Width = col_double(),
##   Species = col_character()
## )

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)

## 
##  DESCRIPTIVES
## 
##  Descriptives                                          
##  ───────────────────────────────────────────────────── 
##                           Species       Sepal.Length   
##  ───────────────────────────────────────────────────── 
##    N                      setosa                  50   
##                           versicolor              50   
##                           virginica               50   
##    Missing                setosa                   0   
##                           versicolor               0   
##                           virginica                0   
##    Mean                   setosa                5.01   
##                           versicolor            5.94   
##                           virginica             6.59   
##    Std. error mean        setosa              0.0498   
##                           versicolor          0.0730   
##                           virginica           0.0899   
##    Median                 setosa                5.00   
##                           versicolor            5.90   
##                           virginica             6.50   
##    Mode                   setosa                5.00   
##                           versicolor            5.50   
##                           virginica             6.30   
##    Sum                    setosa                 250   
##                           versicolor             297   
##                           virginica              329   
##    Standard deviation     setosa               0.352   
##                           versicolor           0.516   
##                           virginica            0.636   
##    Variance               setosa               0.124   
##                           versicolor           0.266   
##                           virginica            0.404   
##    Range                  setosa                1.50   
##                           versicolor            2.10   
##                           virginica             3.00   
##    Minimum                setosa                4.30   
##                           versicolor            4.90   
##                           virginica             4.90   
##    Maximum                setosa                5.80   
##                           versicolor            7.00   
##                           virginica             7.90   
##    Skewness               setosa               0.120   
##                           versicolor           0.105   
##                           virginica            0.118   
##    Std. error skewness    setosa               0.337   
##                           versicolor           0.337   
##                           virginica            0.337   
##    Kurtosis               setosa              -0.253   
##                           versicolor          -0.533   
##                           virginica           0.0329   
##    Std. error kurtosis    setosa               0.662   
##                           versicolor           0.662   
##                           virginica            0.662   
##    25th percentile        setosa                4.80   
##                           versicolor            5.60   
##                           virginica             6.23   
##    50th percentile        setosa                5.00   
##                           versicolor            5.90   
##                           virginica             6.50   
##    75th percentile        setosa                5.20   
##                           versicolor            6.30   
##                           virginica             6.90   
##  ─────────────────────────────────────────────────────

# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

# library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")

Frequencies

Variable: iris$Species
Type: Factor (unordered)

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)

	Freq	%	% Cum.
setosa	50	33.33	33.33
versicolor	50	33.33	66.67
virginica	50	33.33	100.00
Total	150	100.00	100.00

with(tobacco, print(ctable(smoker, diseased), method = 'render'))

Cross-Tabulation / Row Proportions

Variables: smoker * diseased
Data Frame: tobacco

	diseased
smoker	Yes	No	Total
Yes	125 (41.95%)	173 (58.05%)	298 (100.00%)
No	99 (14.10%)	603 (85.90%)	702 (100.00%)
Total	224 (22.40%)	776 (77.60%)	1000 (100.00%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-19

with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-19

summarytools::descr(iris, style = "rmarkdown")

Non-numerical variable(s) ignored: Species

Descriptive Statistics

Data Frame: iris
N: 150

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
Mean	5.84	3.06	3.76	1.20
Std.Dev	0.83	0.44	1.77	0.76
Min	4.30	2.00	1.00	0.10
Q1	5.10	2.80	1.60	0.30
Median	5.80	3.00	4.35	1.30
Q3	6.40	3.30	5.10	1.80
Max	7.90	4.40	6.90	2.50
MAD	1.04	0.44	1.85	1.04
IQR	1.30	0.50	3.50	1.50
CV	0.14	0.14	0.47	0.64
Skewness	0.31	0.31	-0.27	-0.10
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-0.61	0.14	-1.42	-1.36
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")

Non-numerical variable(s) ignored: Species

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.84	0.83	4.30	5.80	7.90
Sepal.Width	3.06	0.44	2.00	3.00	4.40
Petal.Length	3.76	1.77	1.00	4.35	6.90
Petal.Width	1.20	0.76	0.10	1.30	2.50

view(dfSummary(iris))

Method 'viewer' only valid within RStudio. Switching method to 'browser'.

Output file written: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//Rtmp1Mxgoj/file3e4b6cfc08d1.html

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")

Data Frame Summary

tobacco
N: 1000

No	Variable	Stats / Values	Freqs (% of Valid)	Text Graph	Valid	Missing
1	gender [factor]	1. F 2. M	489 (50.0%) 489 (50.0%)	IIIIIIIIIIIIIIII IIIIIIIIIIIIIIII	978 (97.8%)	22 (2.2%)
2	age [numeric]	mean (sd) : 49.6 (18.29) min < med < max : 18 < 50 < 80 IQR (CV) : 32 (0.37)	63 distinct values		975 (97.5%)	25 (2.5%)
3	age.gr [factor]	1. 18-34 2. 35-50 3. 51-70 4. 71 +	258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%)	IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIIII	975 (97.5%)	25 (2.5%)
4	BMI [numeric]	mean (sd) : 25.73 (4.49) min < med < max : 8.83 < 25.62 < 39.44 IQR (CV) : 5.72 (0.17)	974 distinct values		974 (97.4%)	26 (2.6%)
5	smoker [factor]	1. Yes 2. No	298 (29.8%) 702 (70.2%)	IIIIII IIIIIIIIIIIIIIII	1000 (100%)	0 (0%)
6	cigs.per.day [numeric]	mean (sd) : 6.78 (11.88) min < med < max : 0 < 0 < 40 IQR (CV) : 11 (1.75)	37 distinct values		965 (96.5%)	35 (3.5%)
7	diseased [factor]	1. Yes 2. No	224 (22.4%) 776 (77.6%)	IIII IIIIIIIIIIIIIIII	1000 (100%)	0 (0%)
8	disease [character]	1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ]	36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.5%)	IIIIIIIIIIIIIIII IIIIIIIIIIIIIII IIIIIIIII IIIIIIII IIIIIIII IIIIIIII IIIIII IIIIII IIIII IIII IIIIIIIII	222 (22.2%)	778 (77.8%)
9	samp.wgts [numeric]	mean (sd) : 1 (0.08) min < med < max : 0.86 < 1.04 < 1.06 IQR (CV) : 0.19 (0.08)	0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded	IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIII	1000 (100%)	0 (0%)

# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")

Descriptive Statistics

Data Frame: iris
Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.01	0.35	4.30	5.00	5.80
Sepal.Width	3.43	0.38	2.30	3.40	4.40
Petal.Length	1.46	0.17	1.00	1.50	1.90
Petal.Width	0.25	0.11	0.10	0.20	0.60

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50

view(iris_stats_by_species)

Output file written: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//Rtmp1Mxgoj/file3e4b3ee1d2fd.html

Method 'viewer' only valid within RStudio. Switching method to 'browser'.

Output file appended: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//Rtmp1Mxgoj/file3e4b3ee1d2fd.html

data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

Descriptive Statistics

Variable: tobacco$BMI by age.gr

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

	Mean	Std.Dev	Min	Median	Max
18-34	23.84	4.23	8.83	24.04	34.84
35-50	25.11	4.34	10.35	25.11	39.44
51-70	26.91	4.26	9.01	26.77	39.21
71 +	27.45	4.37	16.36	27.52	38.37

tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)
view(freq_tables, footnote = NA, file = 'freq-tables.html')

Output file written: freq-tables.html

Method 'viewer' only valid within RStudio. Switching method to 'browser'.

Output file appended: freq-tables.html

what.is(iris)

$properties property value 1 class data.frame 2 typeof list 3 mode list 4 storage.mode list 5 dim 150 x 5 6 length 5 7 is.object TRUE 8 object.type S3 9 object.size 7256 Bytes

$attributes.lengths names class row.names 5 1 150

$extensive.is [1] “is.data.frame” “is.list” “is.object” “is.recursive” [5] “is.unsorted”

freq(tobacco$gender, style = 'rmarkdown')

## ### Frequencies   
## **Variable:** tobacco$gender     
## **Type:** Factor (unordered)   
## 
## |     &nbsp; | Freq | % Valid | % Valid Cum. | % Total | % Total Cum. |
## |-----------:|-----:|--------:|-------------:|--------:|-------------:|
## |      **F** |  489 |   50.00 |        50.00 |   48.90 |        48.90 |
## |      **M** |  489 |   50.00 |       100.00 |   48.90 |        97.80 |
## | **\<NA\>** |   22 |         |              |    2.20 |       100.00 |
## |  **Total** | 1000 |  100.00 |       100.00 |  100.00 |       100.00 |

print(freq(tobacco$gender), method = 'render')

Frequencies

Variable: gender
Type: Factor (unordered)

		Valid		Total
gender	Freq	%	% Cumul	%	% Cumul
F	489	50.00	50.00	48.90	48.90
M	489	50.00	100.00	48.90	97.80
<NA>	22			2.20	100.00
Total	1000	100.00	100.00	100.00	100.00

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-19

skimr

library(skimr)
skim(df)

DataExplorer

library(DataExplorer)
DataExplorer::create_report(df)

Grafikler

library(ggplot2)
library(mosaic)

## Loading required package: lattice

## Loading required package: ggformula

## Loading required package: ggstance

## 
## Attaching package: 'ggstance'

## The following objects are masked from 'package:ggplot2':
## 
##     geom_errorbarh, GeomErrorbarh

## 
## New to ggformula?  Try the tutorials: 
##  learnr::run_tutorial("introduction", package = "ggformula")
##  learnr::run_tutorial("refining", package = "ggformula")

## Loading required package: mosaicData

## Loading required package: Matrix

## 
## Attaching package: 'Matrix'

## The following object is masked from 'package:tidyr':
## 
##     expand

## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.
## 
## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.

## 
## Attaching package: 'mosaic'

## The following object is masked from 'package:Matrix':
## 
##     mean

## The following object is masked from 'package:questionr':
## 
##     prop

## The following objects are masked from 'package:dplyr':
## 
##     count, do, tally

## The following object is masked from 'package:purrr':
## 
##     cross

## The following object is masked from 'package:ggplot2':
## 
##     stat

## The following objects are masked from 'package:stats':
## 
##     binom.test, cor, cor.test, cov, fivenum, IQR, median,
##     prop.test, quantile, sd, t.test, var

## The following objects are masked from 'package:base':
## 
##     max, mean, min, prod, range, sample, sum

# mPlot(irisdata)

ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')

## ### Cross-Tabulation / Row Proportions   
## **Variables:** gender * smoker     
## **Data Frame:** tobacco   
##    
## |        |        |              |              |                |
## |-------:|-------:|-------------:|-------------:|---------------:|
## |        | smoker |          Yes |           No |          Total |
## | gender |        |              |              |                |
## |      F |        | 147 (30.06%) | 342 (69.94%) |  489 (100.00%) |
## |      M |        | 143 (29.24%) | 346 (70.76%) |  489 (100.00%) |
## | \<NA\> |        |   8 (36.36%) |  14 (63.64%) |   22 (100.00%) |
## |  Total |        | 298 (29.80%) | 702 (70.20%) | 1000 (100.00%) |

print(ctable(tobacco$gender, tobacco$smoker), method = 'render')

Cross-Tabulation / Row Proportions

Variables: gender * smoker
Data Frame: tobacco

	smoker
gender	Yes	No	Total
F	147 (30.06%)	342 (69.94%)	489 (100.00%)
M	143 (29.24%)	346 (70.76%)	489 (100.00%)
<NA>	8 (36.36%)	14 (63.64%)	22 (100.00%)
Total	298 (29.80%)	702 (70.20%)	1000 (100.00%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2018-12-19

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018

Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018

Rcmdr

library(Rcmdr)

Rcmdr::Commander()

A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

Sonraki Konular

RStudio ile GitHub
Hipotez testleri
R Markdown ve R Notebook ile tekrarlanabilir rapor

Diğer kodlar

Diğer kodlar için bakınız: https://sbalci.github.io/

Geri Bildirim

Geri bildirim için tıklayınız: Geri bildirim formu

Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩

R ile analize başlarken1

Derleyen Serdar Balcı, MD, Pathologist

2018-12-19

R yükleme

R-project

RStudio

RStudio eklentileri

X11

Java OS

R zor şeyler için kolay, kolay şeyler için zor

R paketleri

Neden paketler var

Paketleri nereden bulabiliriz

Kendi paket evrenini oluştur

R için yardım bulma

R paket yükleme

R studio ile proje oluşturma

RStudio ile veri yükleme

Excel

SPSS

csv

Veriyi görüntüleme

Veriyi değiştirme

Veriyi kod ile değiştirelim

Veriyi eklentilerle değiştirme

RStudio aracılığıyla recode

Basit tanımlayıcı istatistikler

summarytools

Frequencies

Cross-Tabulation / Row Proportions

Descriptive Statistics

Data Frame Summary

Descriptive Statistics

Descriptive Statistics

Frequencies

skimr

DataExplorer

Grafikler

Cross-Tabulation / Row Proportions

Rcmdr

jamovi

Sonraki Konular

Diğer kodlar

Geri Bildirim

R ile analize başlarken¹